Wang, X.; Liu, C.; Sun, B.; Ponge, D.; Jiang, C.; Raabe, D.: The dual role of martensitic transformation in fatigue crack growth. Proceedings of the National Academy of Sciences of the United States of America 119 (9), e2110139119 (2022)
Lee, D.-H.; Zhao, Y.; Lee, S. Y.; Ponge, D.; Aime Jägle, E.: Hydrogen-assisted failure in Inconel 718 fabricated by laser powder bed fusion: The role of solidification substructure in the embrittlement. Scripta Materialia 207, 114308 (2022)
Wang, Z.; Lu, W.; Min Song, F. A.; Ponge, D.; Raabe, D.; Li, Z.; Li, Z.: High stress twinning in a compositionally complex steel of very high stacking fault energy. Nature Communications 13, 3598 (2022)
Aota, L. S.; Bajaj, P.; Zilnyk, K. D.; Ponge, D.; Zschommler Sandim, H. R.: The origin of abnormal grain growth upon thermomechanical processing of laser powder-bed fusion alloys. Materialia 20, 101243 (2021)
Varanasi, R. S.; Zaefferer, S.; Sun, B.; Ponge, D.: Localized deformation inside the Lüders front of a medium manganese steel. Materials Science and Engineering A: Structural Materials Properties Microstructure and Processing 824, 141816 (2021)
Scientists of the Max-Planck-Institut für Eisenforschung pioneer new machine learning model for corrosion-resistant alloy design. Their results are now published in the journal Science Advances
Complex simulation protocols combine distinctly different computer codes and have to run on heterogeneous computer architectures. To enable these complex simulation protocols, the CM department has developed pyiron.
Statistical significance in materials science is a challenge that has been trying to overcome by miniaturization. However, this process is still limited to 4-5 tests per parameter variance, i.e. Size, orientation, grain size, composition, etc. as the process of fabricating pillars and testing has to be done one by one. With this project, we aim to…
Atom probe tomography (APT) provides three dimensional(3D) chemical mapping of materials at sub nanometer spatial resolution. In this project, we develop machine-learning tools to facilitate the microstructure analysis of APT data sets in a well-controlled way.
Ever since the discovery of electricity, chemical reactions occurring at the interface between a solid electrode and an aqueous solution have aroused great scientific interest, not least by the opportunity to influence and control the reactions by applying a voltage across the interface. Our current textbook knowledge is mostly based on mesoscopic…